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The Journal of Neuroscience, August 15, 2002, 22(16):7206-7217
Genetic Influence on Quantitative Features of Neocortical
Architecture
Matthias
Kaschube1, 2, *,
Fred
Wolf1, 3, *,
Theo
Geisel1, and
Siegrid
Löwel2
1 Max-Planck-Institut für
Strömungsforschung and Fakultät für Physik,
Universität Göttingen, 37073 Göttingen, Germany,
2 Forschergruppe "Visuelle Entwicklung und
Plastizität," Leibniz-Institut für Neurobiologie, 39118 Magdeburg, Germany, and 3 Institute for Theoretical
Physics, University of California, Santa Barbara, California 93106
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ABSTRACT |
The layout of functional cortical maps exhibits a high degree of
interindividual variability that may account for individual differences
in sensory and cognitive abilities. By quantitatively assessing the
interindividual variability of orientation preference columns in the
primary visual cortex, we demonstrate that column sizes and shapes as
well as a measure of the homogeneity of column sizes across the visual
cortex are significantly clustered in genetically related animals and
in the two hemispheres of individual brains. Taking the developmental
timetable of column formation into account, our data indicate a
substantial genetic influence on the developmental specification of
visual cortical architecture and suggest ways in which genetic
information may influence an individual's visual abilities.
Key words:
visual cortex; development; orientation columns; cortical
maps; area 17; genetic determination
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INTRODUCTION |
In most areas of the cerebral
cortex, information is processed in two-dimensional (2D) arrays of
functional modules, called cortical columns (LeVay and Nelson,
1991 ; Creutzfeldt, 1995). Neurons in individual
columns are densely connected by local intracortical circuits and share
many functional properties (e.g., stimulus selectivities in sensory
cortical areas or movement specificities in motor cortical areas). In a
plane parallel to the cortical surface, neuronal selectivities vary
systematically, so that columns of similar functional properties form
highly organized 2D patterns, known as functional cortical maps. This
2D organization of a cortical area appears closely related to its
intrinsic circuitry and computational capabilities: the organization of
intracortical synaptic connections is tightly matched to the exact
spatial arrangement of functional columns (Somogyi et al.,
1998), and improvements of both sensory and motor performance
have repeatedly been linked to learning-induced plasticity of column
arrangements (Recanzone, 2000). Many lines of evidence
suggest that during the ontogenetic development of the cerebral cortex,
functional maps typically form through activity-dependent refinement of
initially crude patterns of synaptic connections (Stryker,
1991; Goodman and Shatz, 1993; Singer,
1995; Price and Willshaw, 2000). Therefore,
epigenetic factors such as spontaneously generated patterns of neuronal
activity (Weliky and Katz, 1999) or individual
experience (Blakemore and Cooper, 1970; Singer et al., 1981; Frégnac and Imbert, 1984; Sengpiel et
al., 1999) are widely believed to play a decisive role in
specifying the precise layout of functional cortical maps. Recently,
however, a series of experiments indicated that the initial development
of these maps is much less dependent on experience than previously
thought (Gödecke and Bonhoeffer, 1996;
Crair et al., 1998; Löwel et al.,
1998; Crowley and Katz, 2000) and raised the
urgency of exploring the largely unknown role of genetic information in
functional cortical development.
To explore the impact of genetic factors on cortical architecture, we
therefore analyzed the variability of patterns of orientation preference columns [OR columns, columns of neurons preferentially responding to visual contours of a particular orientation (Hubel and Wiesel, 1962)] in the primary visual cortex in genetically related and unrelated animals raised in the same visual environment. Using this approach, a strong impact of genetic factors should show up
as a reduced variability (i.e., enhanced similarity of column patterns
in genetically related animals compared with the overall
interindividual variability within the population). To test for such a
reduced variability of column patterns, we used a newly developed image
analysis technique to quantify the basic properties of the layout of
orientation columns in the primary visual cortex. These analyses
revealed that (1) the sizes and shapes of visual cortical orientation
columns exhibit a high degree of interindividual variability, and (2)
column sizes and shapes as well as a measure of the homogeneity of
column sizes across the visual cortex are significantly clustered in
genetically related animals and in the two hemispheres of individual
brains. Taking the developmental timetable of column formation into
account, these observations indicate a substantial genetic influence on the developmental specification of visual cortical architecture.
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MATERIALS AND METHODS |
Animals. We analyzed 2-deoxyglucose (2-DG)-labeled
patterns of OR columns in the primary visual cortex (area 17) of 31 adult cats (48 hemispheres). OR maps of these animals have been
previously published (Löwel et al., 1987, 1988;
Löwel and Singer, 1990). Table
1 lists details of the dataset used in
the present analysis. To assess littermate clustering, data from 22 cats born and raised in the same overall visual environment, namely the
animal house of the Max-Planck-Institut für Hirnforschung
(Frankfurt am Main, Germany), were used. The nursing facilities
consisted of two nearly identical rooms with bar cages and tiled floors
and walls. At all times, the crew of the animal house cared for all
animals, so that no particular person was assigned to only a subset of the animals. Each room contained up to 18 cages, so that animals had
visual and other social contact not only with their littermates and
mothers but with all of the other animals living in the same room as
well. All animals in the colony were mongrels. For impregnation, female
cats were given the opportunity to mate with 2-3 tomcats. Tomcats in
the colony were exchanged regularly. All litters in the sample used in
this study were born and raised by different mothers.
All animals stayed in these rooms until the 2-DG experiments. The 2-DG
experiments were performed between January 31, 1984 and August 25, 1987 by the same main experimenter (S. Löwel) in the same laboratory,
keeping the visual experience of all animals very similar. The visual
stimuli during the 2-DG experiments were always identical in spatial
and temporal frequency and only differed in orientation, whereby
orientation presentation within the two groups (littermates vs
nonlittermates) was rather balanced (see Table 1): in the littermates,
horizontal contours (0°) were used in 57% of cases (12 of 21), and
vertical contours (90°) were used in 43% of cases (9 of 21); in
nonlittermates, 0° were used in 44% of cases (12 of 27), 90° were
used in 37% of cases (10 of 27), 45° were used in 11% of cases (3 of 27), and 135° were used in 7% of cases (2 of 27). The 22 animals
raised in Frankfurt included six litters (one litter of three siblings
and five litters of two siblings). In addition, data from another nine
animals, bought from two animal breeding companies in Germany
(Ivanovas, Kisslegg im Allgäu, Germany; Gaukler, Offenbach,
Germany), were used for the assessment of the overall variability of
parameter values, for the calculation of correlations among various
parameters, and for the calculation of correlations of left and right
hemisphere parameter values.
Image digitization. Photoprints of the 2-DG autoradiographs
were digitized using a flat-bed scanner (OPAL ultra; Linotype-Hell AG,
Eschborn, Germany, operated using Corel Photoshop; Corel, Ottawa,
Ontario, Canada) with an effective spatial resolution of 9.45 pixels/mm cortex and 256 gray levels per pixel. For every autoradiograph, this yielded an array of gray values
I0(x), where x is the
position within the area and I0 its intensity of labeling.
Region of interest. For every autoradiograph, a region of
interest encompassing the pattern labeled in area 17 was defined by
visual inspection. The 17/18 border was identified based on the larger
column spacing and different pattern layout in area 18 compared with
area 17 (Löwel et al., 1987). The manually defined polygon encompassing the entire 2-DG pattern within area 17 was stored
together with every autoradiograph. Only the pattern within area 17 was
used for subsequent analysis. Regions including artifacts (scratches,
folds, and air bubbles) were excluded from further analysis.
Preprocessing. The digitized patterns were preprocessed to
remove overall variations in the intensity of labeling. To achieve this, the local average labeling intensity:
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(1)
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was calculated using the kernel K (y) =  exp ( y2/2  )
and subtracted from the pattern:
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(2)
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The spatial width x of the
kernel was determined from the requirement that structures with a
wavelength of <1.5 mm, the range of column sizes, should not be
strongly attenuated by the preprocessing. We used x = 0.43 mm, for which the attenuation at wavelengths of <1.5 mm is
<20%. This choice was sufficient to remove overall variations in
labeling intensity but left the column pattern unaffected in all
autoradiographs. The pattern I1(x)
was then centered and normalized by subtracting its average gray value
and dividing by the SD of gray values, resulting in an array
I(x) with:
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(3)
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(4)
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Finally, in artifact regions and regions outside of area 17, the
gray values of the patterns were set to zero. Figure
1 shows representative examples of
original and preprocessed patterns.
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Figure 1.
Preprocessing leaves the essential spatial
properties of the 2-DG patterns unaffected. a, Typical 2-DG
pattern. b, Preprocessed 2-DG pattern. c, The
zero contours of the preprocessed 2-DG pattern (yellow
lines) are superimposed on the original autoradiograph in
a. Note that the zero contours closely follow the
outlines of the labeled domains.
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Analysis of column layout. For every autoradiograph, 2D maps
of local column spacing and of a measure of domain anisotropy or
bandedness were estimated based on wavelet transforms (Farge, 1992) of I(x):
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(5)
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Here x,  , l are the position,
orientation, and scale of the wavelet
 x, ,l(y), and
Î(x, , l) denotes
the array of wavelet coefficients. We used complex-valued Morlet-wavelets defined by a mother wavelet:
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(6)
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and
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(7)
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with the rotation matrix
 .
The characteristic wavelength of a wavelet with scale l is
  l with = 2 /|k|. For large values of
|k|, the wavelets in Equation 6 exhibit a
high resolution of spatial frequencies. For small values of |k|, they are localized in the cortical
coordinates enabling high spatial resolution. The parameter
y determines the degree of anisotropy of the
wavelets, with larger values leading to a higher sensitivity for
elongated, bandlike structures. Therefore we used large
|k| wavelets of moderate anisotropy (k = (7, 0), y = 1) to estimate the column spacing and small
|k| wavelets of enhanced anisotropy (k = (2, 0), y = 1.5) to calculate the local anisotropy parameter. With these choices,
patterns with large or small columns and with patchy or bandlike
appearance were well discriminated by the measures described in the
following paragraphs.
Spacing. The orientation averaged modulus:
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(8)
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of the wavelet coefficients was used to estimate the local
column spacing. For every position x, the scale:
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(9)
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maximizing  (x,
l) was determined. The corresponding characteristic
wavelength:
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(10)
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was used as an estimate for the local column spacing at the
position x. For every position (spatial grid size, 0.11 mm),
wavelet coefficients for six orientations i  {0,
/6, ..., 5/6} and 12 scales
lj (with
li equally spaced in 0.5 mm, 2 mm) were calculated. The scale maximizing
(x, l) was then estimated
as the maximum of a polynomial in l fitting the
[(x, lj)] for a
given position x (least square fit).
Bandedness. The orientation dependence of the wavelet
coefficients was used to calculate a parameter measuring the anisotropy of local pattern elements (Fig. 2). For a
pattern consisting of isotropic patches, the wavelet coefficients
depend only weakly on the orientation of the wavelet. For a pattern
consisting of elongated bands, the magnitude of the wavelet
coefficients depends strongly on wavelet orientation and is largest if
the orientation of the wavelet matches the orientation of the bands. Using only coefficients at the local wavelength (x), we therefore calculated:
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(11)
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and used the modulus |s(x)| of the local
average of this quantity:
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(12)
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with
 and = 1.3 <(x)>x as a local
measure of bandedness. Here <(x)>x is the
average local wavelength. Here and in the following,
 · x denotes averaging over all locations in area 17. With this choice of , s(x) is sensitive to the
occurrence of bandlike regions that extend at least over the size of a
hypercolumn. Wavelet coefficients for the scale corresponding to the
local column spacing and for nine orientations i {0, /9, ..., 8/9} were calculated for every
position and used to evaluate Equations 11 and 12. Based on the local
column spacing, (x), and the local bandedness,
|s(x)|, the overall layout of the 2-DG patterns was characterized by four parameters: mean column spacing, = (x)x; the SD of local column spacing
across area 17,  =  , called spacing inhomogeneity in
the following; mean bandedness,  = |s(x)|x; and the SD
of the local anisotropy parameter,  =  , called shape inhomogeneity in
the following.
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Figure 2.
Analyzing the shape of orientation columns.
a, Examples of wavelets ,x with different
orientation superimposed on a stripe-like region of 2-DG-labeled
orientation columns. The real parts of the complex-valued wavelets are
shown. Positive regions are delineated by white lines;
negative regions are delineated by dark lines. The wavelet
of optimal orientation (solid lines) and one example of
nonoptimal orientation (dotted lines) are shown.
b, The normalized squared modulus of the wavelet
coefficients:
as a function of orientation . For a vertically oriented
wavelet, = 0°, 180°. c, Wavelets
,x superimposed on a patchy region of 2-DG-labeled
orientation columns and the respective coefficients (d)
(description as in a and b). Note that for the
stripe-like region, | |2
is strongly modulated and exhibits a pronounced peak at the wavelet
orientation matching the stripe orientation. This is not the case
for the patchy region.
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Accuracy of parameter estimation. To estimate these
parameters reliably and precisely, up to six autoradiographs derived
from flat-mount sections at various cortical depths were analyzed for every brain hemisphere. The parameter values for the spacing parameters and were estimated with an average SE of <20
µm (Table 2). The dimensionless shape
parameters and , which range between 0.06 and
0.3, were estimated with an average SE of <0.01 (Table 2).
Qualitatively and quantitatively the results reported were insensitive
to variation of the parameters of the image analysis method such as
, x, k , and the
number of used wavelet orientations and scales.
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Table 2.
Accuracy of estimation for the layout parameters mean
column spacing (), spacing inhomogeneity (),
bandedness (), and shape inhomogeneity () using up
to six 2-DG autoradiographs obtained from various cortical depths
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Permutation tests. Because the distributions of parameter
values were not Gaussian, permutation tests were used to assess the
statistical significance of littermate clustering, correlations of
parameter values of left and right hemispheres, and correlations between parameters. The approach can be summarized as follows: To
assess whether parameter values in littermates were significantly clustered, we compared the similarity of parameter values among real
littermates with the similarity of parameter values found in randomly
chosen sets of animals raised in the same environment. Statistically,
there is significant littermate clustering if the hypothesis:
"Parameter values are independent of an animals' genetic identity"
is rejected (i.e., if the observed degree of similarity is extremely
unlikely to occur by chance). To test this hypothesis, we calculated a
single number [the average distance to the litter mean (ADLM), see
below] that quantifies the similarity of parameter values among
littermates and compared its actual value with the statistical
distribution predicted by the hypothesis that the parameter values are
independent of an animal's genetic identity.
P values were calculated using 106
randomly generated permutations of the data. For the analysis of
littermate clustering, we calculated the ADLM  Q for
parameter Q {, , ,
} according to:
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(13)
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where i indexes hemispheres,
l(i) is the index of the litter to which
hemisphere i belongs,
 l(i) is the average of
parameter values within this litter, and N is the total
number of hemispheres in all litters. Small values of Q
indicate littermate clustering. If parameter values in genetically
related animals or hemispheres are statistically independent (i.e.,
genetic information plays no role in the specification of parameter
Q), the probability to observe an ADLM as small as or
smaller than Q is:
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(14)
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where Qp =  i|Qp(i) l(p(i))|/N
is the ADLM of pseudolitters generated by a suitable permutation
p(i) of hemisphere indices,  ( ) is the
Heaviside function, and p denotes the
average over permutations. p values were calculated using
either arbitrary random permutation (PII)
or only such permutations that preserved left-right pairs of
hemisphere indices (PI). Histograms of
Qp values for these two randomization
schemes are displayed in Figure 10i-l. P values for
correlation coefficients were calculated analogous to the calculation
of PII values.
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RESULTS |
Using a newly developed technique based on wavelet analysis, we
quantified the major layout properties of OR column patterns obtained
by 2-DG autoradiography in the cat primary visual cortex (area 17)
(Löwel et al., 1987, 1988; Löwel and
Singer, 1990; Kaschube et al., 2000). In all
investigated hemispheres (48 hemispheres from 31 animals), OR columns
preferring the same stimulus orientation were arranged in complex
repetitive patterns (Fig. 3a).
Individual columns may be widely spaced or closely spaced and may be
shaped isotropically or anisotropically, appearing as circular patches or as elongated bands (Fig. 3b). For every pattern, we
calculated a 2D map of local column spacing and a 2D map of local
column anisotropy, measuring the anisotropy of column shapes (large
values for bandlike patterns, small values for patchy patterns).
Based on these maps, we calculated four parameters describing the
overall spatial organization of the OR patterns (Fig. 3c,d):
(1) mean column spacing , (2) SD of local column
spacings across area 17 (spacing inhomogeneity), (3) mean bandedness
, and (4) SD of the local anisotropy parameter
(shape inhomogeneity). Mean column spacing and bandedness measure
whether a pattern predominantly contains large or small and bandlike or
patchy OR columns, respectively; spacing inhomogeneity and shape
inhomogeneity quantify pattern inhomogeneity across area 17.
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Figure 3.
Layout properties of 2-DG-labeled OR domains in
the primary visual cortex (area 17) of cats and their quantification.
a, Representative examples from the analyzed data pool:
2-DG-labeled OR domains appear as dark gray to black
patches or stripes on a lighter gray
background. The 2-DG patterns were visualized on cortical
flatmount sections and thus contain OR domains within the entire area
17. Anterior is at the top of each figure, and posterior is
at the bottom. lat, Lateral; med,
medial. b, Variability of pattern properties. OR
domains vary in both the spacing of adjacent domains (left
column, small; right column, large) and in the degree
of anisotropy of domain shape (top row, band-like;
bottom row, patchy). c, d, Quantitative analysis
of the spacing (c) and shape (d) of OR columns.
c, 2D map of local column spacing (x)
(left) of a representative 2-DG pattern (left pattern in
a) obtained by wavelet analysis, coded in
grayscale: light gray regions exhibit
larger-than-average spacing; dark gray regions exhibit
smaller-than-average spacing. In the histogram of local column spacings
(right), the mean column spacing and the SD
of local column spacings (spacing inhomogeneity) are
marked by red and blue arrows, respectively.
d, Local anisotropy parameter s(x) (yellow
bars) superimposed on the analyzed 2-DG pattern (left):
The lengths of the yellow bars are proportional to the
measure of local bandedness |s(x)|, with
long bars indicating bandlike regions and short
bars indicating patchy regions of the pattern. The bars
are oriented perpendicular to the calculated local band orientation.
The histogram of |s(x)| (right)
exhibits a broad peak with low and high values of |s|
corresponding to patchy and bandlike regions in the 2-DG pattern. The
mean bandedness and the SD of local bandedness
(shape inhomogeneity) are marked by red and blue
arrows, respectively. Scale bars: a, c, d, 10 mm;
b, 5 mm.
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Because an enhanced similarity among relatives can only be detected for
parameters that exhibit considerable interindividual variability, we
assessed the variability of these parameters across the population. As
illustrated in Figure 4, all four
parameters fulfill this requirement. Furthermore, patterns judged
subjectively as similar exhibited similar parameter values. Figures
5 and 7 show examples of pairs of similar
patterns of OR columns obtained from the two hemispheres of individual
brains (Fig. 5) and from the brains of littermates (Fig. 7).
Subjectively dissimilar patterns also exhibited substantially different
parameter values (see Fig. 9). 2D maps of local column spacing and of
local column anisotropy are displayed in Figure
6 for the patterns of OR columns obtained from the two hemispheres of individual brains in Figure 5 and in Figure
8 for the patterns from littermates shown in Figure 7. Although the patterns have similar
average parameter values, their 2D parameter maps are not
identical.
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Figure 4.
Interindividual variability (a, b, d,
e) and relative independence (c, f) of the
spacing and shape parameters mean spacing (; a), spacing
inhomogeneity (; b), bandedness (;
d), and shape inhomogeneity (;
e) in 48 hemispheres from 31 animals. Values from individual
hemispheres are indicated by ×s arranged along the x-axis
in a, b, d, and e. Error bars show the SEM of the
estimated parameter values. Scatter plots of the spacing and shape
parameters are displayed in c and f. Note that
the values of all four parameters display significant interindividual
variability: Mean column spacings vary between 1.0 and 1.4 mm
(a) and spacing inhomogeneities () vary
between 0.1 and 0.27 mm (b) in different animals. The shape
parameters and exhibit an even larger
interindividual variability: Mean bandedness varies by more than a
factor of two between 0.14 for very patchy patterns and 0.3 for
patterns largely composed of bands (d). Shape inhomogeneity
varies between 0.06 and 0.14 (e). The
dashed-dotted lines in a, b,
d, and e mark the total range of interindividual
variability.
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Figure 5.
Examples of the similarity of patterns of
2-DG-labeled orientation columns in the two hemispheres of individual
animals. The patterns of both the left (a, c) and right
(b, d) hemispheres of cats C16 (a, b) and C1
(c, d) are displayed in a way so that the 17/18 border
appears left in all panels (to this end, the
right hemisphere patterns were mirror-inversed) to aid comparison. Note
that the general appearance of the orientation column patterns
(patchiness or bandedness of the pattern, spacing of adjacent domains,
etc.) looks rather similar in the left and right hemispheres of both
animals. Note furthermore that the quantified parameters (column
spacing and bandedness ) quantitatively reflect this similarity:
Column spacing in the left and right area 17 of cat C16 was 1.05 and
1.09 mm, bandedness was 0.30 and 0.28, respectively. In cat C1, column
spacings in the left and right area 17 were 1.10 and 1.06 mm;
bandedness was 0.23 in both hemispheres. In the illustrated cases,
column spacing differed by only 40 µm in the left and right
hemisphere of individual brains, whereas column spacing may differ by
up to 400 µm among hemispheres from unrelated animals. Similarly,
bandedness differed by 0.02 at most in the illustrated hemisphere
pairs, whereas this parameter may differ by up to 0.15 among
hemispheres from unrelated animals.
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Figure 6.
2D maps of local column spacing (a, b, e,
f) and local bandedness (c, d, g, h) for the
patterns obtained from left-right pairs of hemispheres displayed in
Figure 5. The maps are arranged as in Figure 5: the maps in
a and c were derived from the pattern in Figure
5a, the maps in b and d were derived
from the pattern in Figure 5b, the maps in e and
g were derived from the pattern in Figure 5c, and
the maps in f and h were derived from the pattern
in Figure 5d.
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Figure 7.
Examples of the similarity of patterns of
2-DG-labeled orientation columns in littermates. The orientation column
patterns of the related cats C4 and C5 (a, b) and C6 and C7
(c, d) are displayed such that the 17/18 border appears
left in all panels. Note that the general
appearance of the orientation column patterns (patchiness or bandedness
of the pattern, spacing of adjacent domains, etc.) is rather similar in
the littermates. Note furthermore that the quantified parameters
(column spacing and bandedness ) quantitatively reflect this
similarity: Column spacing in the right area 17 of cats C4 and C5 was
1.10 and 1.12 mm, and bandedness was 0.26 and 0.24, respectively. In
the littermate cats C6 and C7, column spacings in the right area 17 were 1.23 mm for both animals, and bandedness was 0.24 and 0.23, respectively. As for pairs of left and right hemispheres from
individual animals (Fig. 5), the differences in parameter values in
littermates are very small compared with the overall interindividual
variability (compare Fig. 4).
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We subsequently compared the spacing and shape parameters of OR maps in
the two hemispheres of 17 brains (Fig.
10a-d).
Because the two hemispheres of a brain are genetically identical,
genetically controlled features of visual cortical architecture are
expected to be similar in the two brain hemispheres. Indeed, our data
show similar parameter values in left and right areas 17. For mean column spacing, spacing inhomogeneity, and bandedness, the parameter values of left and right hemispheres displayed statistically
significant correlations (: r = 0.78, p = 0.0002; : r = 0.49, p = 0.02; : r = 0.46, p = 0.03). The observed interhemispheric correlations are thus consistent with a substantial genetic influence on the developmental specification of visual cortical OR maps.
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Figure 8.
2D maps of local column spacing (a, b, e,
f) and local bandedness (e, d, g, h) for the
patterns in littermates displayed in Figure 7. The maps are arranged as
in Figure 7: the maps in a and c were derived
from the pattern in figure 7a, the maps in b and
d were derived from the pattern in Figure 7b, the
maps in e and g were derived from the pattern in
Figure 7c, and the maps in f and h
were derived from the pattern in Figure 7d.
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Figure 9.
Examples of the dissimilarity of orientation
column patterns in unrelated cats (cats C24 and C7). Arrangement of the
patterns is as in Figures 5 and 7. Column spacing in the two cats was
1.09 and 1.23 mm, respectively; anisotropy was 0.30 and 0.23, respectively, indicating very dissimilar patterns (compare with Figs. 5
and 7).
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Figure 10.
Clustering of parameter values in left and right
hemispheres and among littermates. a, e, i, Mean column
spacing . b, f, j, Spacing inhomogeneity
. c, g, k, Bandedness . d, h,
l, Shape inhomogeneity . a-d,
Comparison of the parameter values in the left and right visual
cortices of 17 animals. Left and right hemisphere values are marked by
×s and  s, respectively. For comparison, the distributions of
parameter values from all hemispheres are plotted on the right
side of the figures (horizontal lines). Note that
values in the left and right hemispheres are often rather similar and
that significant correlations were found for mean column spacing,
spacing inhomogeneity, and bandedness, but not for shape inhomogeneity
(r = 0.17; p = 0.26). In addition,
there were no systematic differences in the architecture of left and
right area 17 (i.e., no signs of lateralization for the
measured parameter set) (p > 0.15;
permutation test for the average sign of left-right differences).
e-h, Littermate clustering: Comparison of the
parameter values in six litters. Data points from littermates are
marked by identical symbols (*, ,  ,  , ×, and + from
left to right). For comparison, the distributions
of parameter values from all hemispheres of animals raised in the same
environment are plotted on the right side of the figures
(horizontal lines). Note that values from littermates
cluster, whereby the degree of clustering varies between litters and
for different parameters. i-l, Permutation tests for
genetic influences on quantified parameters of visual cortical OR maps.
Analysis of 6 × 106 randomly generated
pseudolitters demonstrates statistically significant littermate
clustering for mean column spacing (i), spacing
inhomogeneity (j), and bandedness (k): It
is highly unlikely to observe the ADLMs (i),
(j) and (k)
of real litters (arrows) by chance if no genetic component
is present. Littermate clustering observed for shape inhomogeneity
(l) is, however, not statistically
significant in our data set (l:
PI = 0.092; PII = 0.083). The ADLM values calculated for the real litters are compared
with two distributions obtained by either (I) assigning animals raised
in the same environment to random pseudolitters (left
histograms; see i or (II) first forming pseudobrains of
randomly chosen hemispheres, which were then assigned to pseudolitters
(right histograms; see i). If correlations of
left and right hemisphere parameter values are of entirely epigenetic
origin, randomization scheme I applies and extinguishes all genetic
influences. However, if these correlations are of genetic origin,
randomization scheme II extinguishes all genetic influences by
generating pseudobrains with uncorrelated parameter values in the left
and right hemispheres. For all four parameters, the real ADLMs are
smaller than the large majority of ADLMs calculated from randomized
data.
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Alternatively, the similarity of measured parameters in left and right
visual cortices may reflect the fact that the two hemispheres of one
brain receive visual experiences that are typically more similar than
the experiences of two different animals even if they are raised in the
same environment. However, this ambiguity can be resolved if the
parameter values are also significantly clustered in littermates
compared with unrelated animals. In Figure 10e-h, the
values of the spacing and shape parameters of OR patterns from six
litters are compared with one another and with the overall variability
among all animals raised in the same environment as the littermates. In
the littermates, the parameter values cluster, whereby the degree of
clustering varies between litters and for different parameters (Fig.
10e-h). For instance, spacings in littermates typically
differed by <80 µm, while they may differ by up to 400 µm in
genetically unrelated animals. To test whether the observed degree of
littermate clustering is sufficient to demonstrate a significant
influence of genetic identity on the developmental specification of
these parameters, we calculated the expected distributions of the ADLM
of the parameter values assuming that parameters are statistically
independent of the genetic identity of a hemisphere. To this end, the
original data were randomized such that genetic relationships (I) among
animals or (II) among all hemispheres were extinguished. For all four
parameters, the actual ADLM values were smaller than the average value
in randomized data, indicating littermate clustering (Fig.
10i-l). For mean column spacing, spacing
inhomogeneity, and bandedness, littermate clustering was significant
under both assumptions 1 and 2 (Fig. 10i, :
PI = 0.0046, PII = 0.00014; Fig.10j,
: PI = 0.0028, PII = 0.0005; Fig.10k, :
PI = 0.035; PII = 0.0023).
To assess whether the observed littermate clustering of OR patterns may
be caused by unspecific genetic factors controlling the animal's size
or the size of its brain, we calculated the correlations of all spacing
and shape parameters with the animal's weight and with the size of
area 17. With the exception of a weak correlation of column spacing and
area size (r = 0.34; p = 0.03), there
were no significant correlations. There were also no
significant correlations between the four parameters and
the ages of the animals or the orientation of the visual stimuli. Our
data therefore do not support the idea that unspecific genetic or
experimental influences are responsible for the clustering of
quantitative features of functional cortical architecture in littermates.
How does the observed substantial variability in the layout of visual
cortical orientation columns affect visual information processing?
Because of the retinotopic organization of area 17, each orientation
hypercolumn processes information from a localized region of visual
space. One might therefore imagine that the spatial resolution with
which contour information is analyzed in area 17 is constrained by the
total number of orientation hypercolumns in this area. This number, in
turn, depends on column spacing and area size and can be estimated by
the ratio of area size A and hypercolumn size
2 (there is about one OR column per area
2 of cortical surface). The size of area 17 varied between 330 and 575 mm2, and the size of
hypercolumns varied between 1.0 and 1.7 mm2 in
different hemispheres (Fig.
11a), demonstrating that
both hypercolumn and area size exhibited pronounced interindividual
variability with an approximately twofold range between smallest and
largest values. Because hypercolumn and area size were only weakly
correlated (r = 0.34; p = 0.035) (Fig.
11a), the total number of orientation hypercolumns also
exhibited considerable interindividual variability, ranging between 240 and 410 in different animals (Fig. 11b). Thus cats exhibit
smaller or larger orientation columns mostly regardless of whether they
have a small or a large area 17. Considered individually, both
hypercolumn and area size thus control considerable fractions of the
interindividual variability of hypercolumn number (Fig. 11
b,c). Together, these observations indicate that genetic
control of mean column spacing might effectively mediate a genetic
influence on an animal's visual ability by controlling the number of
visual cortical hypercolumns.
View larger version (10K):
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Figure 11.
The total number of orientation
hypercolumns (HCs) in cat area 17 is determined by the
column spacing and the area size A. a,
Scatter plot of HC size 2 versus area size A.
Note that there is only a weak correlation of HC size and area size
(r = 0.34; p = 0.035), indicating that
larger areas 17 do not necessarily contain larger HCs. b, c,
Scatter plots of the total number of HCs in area 17 (A/2) versus HC size (b)
(r = 0.51; p = 0.001) and versus area
size (c) (r = 0.63; p = 0.0001). Dash-dotted lines are regression lines. Note that
both HC size and area size explain substantial fractions of the
interindividual variability in HC number (2, 26%;
A, 39%), indicating that, on average, large areas 17 and
areas 17 with small HCs contain more modules.
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DISCUSSION |
To our knowledge, our analysis represents the first study that
correlates genetic similarity with quantitative features of functional
cortical architecture. Although the similarity of columnar layouts in
the two hemispheres of individual brains has been noted before for both
monkey ocular dominance columns (Horton and Hocking, 1996) and cat orientation and ocular dominance columns
(Löwel et al., 1988), neither a detailed
quantification of such observations in a large dataset nor an analysis
of similarities and dissimilarities in genetically related and
unrelated animals has been performed before. The present results are
consistent with a substantial genetic influence on several parameters
of visual cortical OR maps: mean spacing, spacing inhomogeneity, and
mean bandedness were significantly more similar in the two hemispheres
of individual brains and in related animals compared with the overall
interindividual variability. The strong interindividual variability and
the similarity of left and right visual cortical patterns observed here
is comparable with the variability and left-right similarity exhibited
by the overall size of representational areas in the primary
somatosensory cortex (Riddle and Purves, 1995) and by
functional modules in the olfactory bulb (Strotmann et al.,
2000; Belluscio and Katz, 2001).
Epigenetic influences
Although it is conceivable in principle that the observed
clustering of parameter values of visual cortical OR maps reflects an
influence of shared nongenetic factors, this possibility appears rather
unlikely if the developmental timetable for the formation of
orientation columns is taken into account: axons from the lateral geniculate nucleus (LGN), providing visual input to visual cortical neurons, enter the cortex at about the time of birth (Ghosh and Shatz, 1992), when many cortical neurons are still migrating to their final positions (Luskin and Shatz, 1985). It is
therefore unlikely that shared intrauterine environments of littermates can specifically influence the development of visual cortical OR maps.
Furthermore, after birth, OR maps that exhibit all of the basic layout
features found in adults form regardless of whether the animals have
normal visual experience (Crair et al., 1998). Finally,
although stripe rearing in early postnatal life increases the
proportion of cells preferring the experienced orientation, major
changes in OR map layout have not been observed (Blakemore and
Cooper, 1970; Singer et al., 1981;
Frégnac and Imbert, 1984; Sengpiel et al.,
1999). Together, these studies indicate that shared experience
is very unlikely to be responsible for the observed similarity of basic
layout parameters of OR maps in genetically related animals.
Comparison with human twin studies
In the context of previous studies of the influence of genetic
factors on morphometric features of the brain, the observation of
significantly similar layouts of visual cortical orientation columns in
related animals comes as a surprise. Although magnetic resonance
imaging studies in humans demonstrated a substantial influence of
genetic factors on "gross" morphometric brain measures such as
intracranial volume, total gray or white matter volume (Baaré et al., 2001), and the overall volume of
specifically chosen brain regions (Tramo et al., 1995,
1998; Pennington et al., 1999; Thompson
et al., 2001), more specific features of brain morphology, such
as sulcal patterns, were surprisingly different between monozygotic
twins and thus did not exhibit a substantial genetic influence
(Steinmetz et al., 1995; Bartley et al.,
1997; Lohmann et al., 1999). These results
suggested that the tightness of genetic control decreases from gross-
to fine-grained features of cortical architecture. However, our present
data quantitatively demonstrate that fine-grained and functionally
relevant aspects of neocortical organization can also be influenced to
a significant degree by genetic information.
Experience dependence
The demonstration of a significant genetic influence on
quantitative features of visual cortical organization has important implications for the study of the impact of experience on cortical organization. Previously, analyses of the effects of visual experience on column layout in the visual cortex have largely ignored the animals' genetic backgrounds. Thus, in the light of our present findings, genetically induced variability may have been interpreted as
evidence for experience dependence in previous studies. For instance,
if the spacing of ocular dominance patterns is subject to a similar
degree of genetic influence as that of orientation columns, the
observation of Löwel (1994) and Tieman and
Tumosa (1997) that animals raised with strabismus or
alternating monocular exposure exhibit an increased spacing of ocular
dominance columns might have been confounded by a genetic dissimilarity
between experimental and control groups. In general, the occurrence of a pronounced interindividual variability together with a substantial genetic influence on visual cortical organization emphasize that to
obtain unambiguous results with respect to the impact of experience on
cortical organization, experimental and control groups must be composed
of littermates.
Mechanisms of genetic control
The similarity of quantitative parameters of visual cortical OR
maps in littermates strongly suggests the existence of developmental mechanisms that mediate a genetic influence on the layout of functional cortical maps. However, the available evidence is insufficient to
determine the precise nature of these mechanisms. Notably, conceivable
mechanisms differ considerably in how directly genetic information
might control visual cortical architecture. The most extreme
possibility that appears consistent with the available data is the
direct genetic prespecification of the orientation preferences of
individual visual cortical neurons by some kind of (to be identified)
molecular recognition mechanisms. Obviously, such a model can explain
our results. Nevertheless, the observed littermate clustering can be
explained just as well by genetic influences on mechanisms for the
activity-dependent selection of cortical circuitry (see below). To
identify the precise mechanism of genetic control, it will be important
for future studies to further characterize the strength and nature of
genetic influences on cortical functional maps. For instance, the
hypothesis of genetic prespecification in detail predicts that maps in
genetically identical animals such as clones or identical twins should
resemble each other in every detail. Consequently, this hypothesis
would be falsified if maps in such animals exhibit similar parameters, which is predicted by our results, but differ in the exact arrangement of the columns. More generally, however, the comparison of maps in
genetically identical animals is a priori incapable of identifying the
mechanisms by which genetic information influences functional cortical
maps (for a detailed discussion, see Miller et al.,
1999). Instead, genuinely new paradigms will be needed to
identify with certainty the mechanisms mediating the genetic control of
functional cortical maps (see Specific versus unspecific genetic factors).
Thus, while our results are in line with recent experiments on visual
cortical development indicating that the initial development of
cortical maps is largely independent of visual experience
(Gödecke and Bonhoeffer, 1996; Crair et
al., 1998; Löwel et al., 1998; Crowley and Katz, 2000), it is important to note that
they are also fully consistent with the hypothesis that the functional architecture of the visual cortex essentially develops through activity-dependent mechanisms (Stryker, 1991;
Goodman and Shatz, 1993). Theoretical studies have
demonstrated that even if cortical columns develop exclusively through
a self-organization process driven by individual experience, genetic
information may determine the final pattern layout by controlling
cellular parameters or boundary and initial conditions (Gierer,
1988, Wolf et al., 1996; Miller et al.,
1999). Various mathematical models for the activity-dependent formation of column patterns predict that the spacing of adjacent columns is determined by cellular parameters such as the width of
dendritic or axonal arborizations (Miller, 1995;
Swindale, 1996). Shape features of column patterns are
influenced by similar parameters (Wolf and Geisel, 1998,
2000). Genetic determination of the parameters of an
activity-dependent developmental process is therefore sufficient to
explain the observed littermate clustering and left/right brain similarities.
In addition, it should be noted that our results do not imply that
column patterns in left-right hemisphere pairs or in littermates are
more or less identical or have identical parameter values. In fact, one
would expect even left-right hemisphere pairs that are genetically
identical to exhibit different parameter values, because identical
parameter values can only result if genetic information is expressed
absolutely symmetrically in the two hemispheres and nongenetic factors
(such as random events in development) do not play any role. Consistent
with this prediction, we indeed find that some of the parameters of
left and right brain hemispheres differ by more than two estimated SEs
and are therefore presumably significantly different.
Specific versus unspecific genetic factors
Although the precise mechanism underlying the observed
littermate clustering of orientation column layout is unknown, our data
suggest that genetic factors control at least a considerable fraction
of the variability of visual cortical columnar architecture. These
genetic factors may either specifically affect the layout of visual
cortical columns (and nothing else) or unspecifically influence a
variety of features of brain organization including column layout.
Because we did not observe strong correlations between the quantified
layout properties and body weight or the size of area 17, our data
indicate that the observed littermate clustering is not caused by
factors unspecifically affecting growth processes throughout the body
or the brain. In particular, because there was only a weak correlation
between column size and area size, our data demonstrate that the total
number of orientation columns is controlled by more than one factor.
However, it is very possible that other unspecific factors do play a
role in orientation column layout. For instance, it is conceivable that genetic factors affecting cellular parameters such as the radius of
dendritic or axonal arborizations contribute to the similarity of
orientation columns in area 17 of littermates. Another possibility is
raised by theoretical studies that demonstrated that in mathematical models of the activity-dependent development of column patterns, the
correlational structure of activity patterns in the LGN affects the
spacing of columns (Goodhill, 1993; Scherf et
al., 1999; Wolf et al., 2000). Therefore, these
models predict that every factor that affects the structure of
correlations within the LGN may also affect the spacing of columns in
the visual cortex. Candidate factors include the number of retinal
ganglion cells, the time of eye opening, and the synaptic organization
of LGN circuitry. To identify the relevant factors, future studies will
have to analyze how the variability of functional cortical architecture correlates with a wide variety of properties at the cellular, circuit,
and systems level.
Genetic control of visual abilities
Our observation that the total number of orientation hypercolumns
in area 17 exhibits a large degree of interindividual variability raises the question of whether genetic control of cortical columnar architecture mediates a genetic influence on an animal's visual abilities. In this context, it is interesting to note that tracing experiments in the human temporal cortex have shown that the
left hemisphere specialization for language processing is
accompanied by a larger number of distinct cortical modules in the
posterior part of Brodmann area 22 (Galuske et al.,
2000). These results suggest that the number of functional
cortical units might indeed be related to the processing capabilities
of an area. In the visual cortex, one can easily imagine that the total
number of orientation hypercolumns directly affects visual
function. Each orientation hypercolumn analyzes contour information
from a small region of visual space. With a larger number of
orientation hypercolumns, contour information can be independently
assessed for more and finer-grained regions of visual space, which
might improve the capacity of the visual system for the processing of
complex images. In other words, a larger number of orientation
hypercolumns would lead to a better "coverage" of contour
orientation and retinotopic position by the cortical map
(Swindale et al., 2000). If visual performance is indeed
affected by the total number of orientation hypercolumns in area 17, then our results would predict a substantial degree of interindividual
variability in those abilities that are mediated by visual cortical
architecture. In this case, genetic control of mean column spacing
could easily mediate a genetic influence on an animal's visual ability
by controlling the number of hypercolumns. At present, however, this
possibility cannot be assessed, because virtually nothing is known
about the degree of interindividual variability in visual abilities
that are mediated by visual cortical circuitry or about their genetic control.
The behavioral genetics of human cognitive abilities has for decades
pointed to a prominent influence of genetic factors on cognitive
performance (Bouchard, 1998) and accordingly on the underlying aspects of brain organization (Thompson et al.,
2001). However, the developmental processes through which
genetic information can influence cortical structure and function, and
thereby an individual's sensory, motor, and cognitive abilities, have
remained essentially unknown to date. Our results suggest that
quantitative features of neocortical architecture such as size and
shape of cortical modules may be key targets of genetic control.
| |
FOOTNOTES |
Received March 5, 2002; revised May 6, 2002; accepted May 15, 2002.
*
M.K. and F.W. contributed equally to this work.
This work was supported by the Wissenschaftsgemeinschaft Gottfried
Wilhelm Leibniz, The National Science Foundation, and the Max-Planck-Gesellschaft. We thank Wolf Singer for permission to use the
2-DG autoradiographs obtained by S.L. in his laboratory for
quantitative analysis. We also thank John M. Crook and Thomas Dresbach
for valuable comments on a previous version of this manuscript and
Steffi Bachmann for excellent technical assistance.
Correspondence should be addressed to Dr. Fred Wolf, Department of
Nonlinear Dynamics, Max-Planck-Institut für
Strömungsforschung, Bunsenstrasse 10, 37073 Göttingen,
Germany. E-mail: fred{at}chaos.gwdg.de.
| |
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March 3, 2009;
106(9):
3555 - 3560.
[Abstract]
[Full Text]
[PDF]
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A. Stepanyants, J. A. Hirsch, L. M. Martinez, Z. F. Kisvarday, A. S. Ferecsko, and D. B. Chklovskii
Local Potential Connectivity in Cat Primary Visual Cortex
Cereb Cortex,
January 1, 2008;
18(1):
13 - 28.
[Abstract]
[Full Text]
[PDF]
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D. L. Adams, L. C. Sincich, and J. C. Horton
Complete Pattern of Ocular Dominance Columns in Human Primary Visual Cortex
J. Neurosci.,
September 26, 2007;
27(39):
10391 - 10403.
[Abstract]
[Full Text]
[PDF]
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D. L. Adams and J. C. Horton
Monocular Cells Without Ocular Dominance Columns
J Neurophysiol,
November 1, 2006;
96(5):
2253 - 2264.
[Abstract]
[Full Text]
[PDF]
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S. D. Faulkner, V. Vorobyov, and F. Sengpiel
Limited Protection of the Primary Visual Cortex from the Effects of Monocular Deprivation by Strabismus
Cereb Cortex,
November 1, 2005;
15(11):
1822 - 1833.
[Abstract]
[Full Text]
[PDF]
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J. C Horton and D. L Adams
The cortical column: a structure without a function
Phil Trans R Soc B,
April 29, 2005;
360(1456):
837 - 862.
[Abstract]
[Full Text]
[PDF]
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T. K. Hensch and M. P. Stryker
Columnar Architecture Sculpted by GABA Circuits in Developing Cat Visual Cortex
Science,
March 12, 2004;
303(5664):
1678 - 1681.
[Abstract]
[Full Text]
[PDF]
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D. L. Adams and J. C. Horton
The Representation of Retinal Blood Vessels in Primate Striate Cortex
J. Neurosci.,
July 9, 2003;
23(14):
5984 - 5997.
[Abstract]
[Full Text]
[PDF]
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D. L. Adams and J. C. Horton
Shadows Cast by Retinal Blood Vessels Mapped in Primary Visual Cortex
Science,
October 18, 2002;
298(5593):
572 - 576.
[Abstract]
[Full Text]
[PDF]
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TOP
ABSTRACT
INTRODUCTION
